MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
Genomics Proteomics Bioinformatics. 2018 Oct;16(5):320-331. doi: 10.1016/j.gpb.2018.08.003. Epub 2018 Dec 18.
Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.
单细胞 RNA 测序 (scRNA-seq) 是一种强大的技术，可以分析单细胞水平的转录组异质性。它是研究细胞亚群和谱系的重要步骤，能够有效地对原始 scRNA-Seq 数据进行低维表示和可视化。在单细胞水平上，转录波动比细胞群体的平均值大得多，并且 RNA 转录本的低量会增加技术丢失事件的速率。因此，scRNA-seq 数据比传统的批量 RNA-seq 数据嘈杂得多。在这项研究中，我们提出了用于 scRNA-seq 数据的深度变分自编码器 (VASC)，这是一种深度多层生成模型，用于 scRNA-seq 数据的无监督降维和可视化。VASC 可以显式地对丢失事件进行建模，并找到原始数据的非线性分层特征表示。在超过 20 个数据集上的测试表明，VASC 在大多数情况下表现出色，与四种最先进的降维和可视化方法相比，它具有更广泛的数据集兼容性。此外，VASC 为 2D 可视化中的非常罕见的细胞群体提供了更好的表示。作为一个案例研究，VASC 成功地重建了植入前胚胎的细胞动力学，并确定了几个与早期胚胎发育相关的候选标记基因。此外，VASC 在具有更多细胞和更高丢失率的 10× Genomics 数据集上也表现良好。
